Analysis method and computer

ABSTRACT

An analysis method performed by a computer analyzes analysis data acquired from a system which includes a plurality of devices to manufacture a product. The computer manages a set of values of each field of the analysis data as a column, analyzes a correspondence from a value belonging to a first target column to a value belonging to a second target column, specifies a surjection column pair which is a set of the first target column and the second target column which have a surjective correspondence, generates graph information to manage a graph of tree structure indicating a connection relation of the column by connecting a graph which has nodes of the first target column and the second target column of the surjection column pair, and performs a yield analysis to specify a parameter affecting an evaluation value using the graph information.

CLAIM OF PRIORITY

The present application claims priority from Japanese patent applicationJP 2018-144427 filed on Jul. 31, 2018, the content of which is herebyincorporated by reference into this application.

BACKGROUND OF THE INVENTION 1. Field of the Invention

The present invention relates to a yield analysis in manufacturingindustries.

2. Description of the Related Art

A semiconductor device and a metal product are manufactured through anumber of manufacturing procedures. In a quality inspection of acomplete product, a defective product and a good product are determined.The yield analysis is performed to specify a factor causing a defectiveproduct, that is, the quality. In the yield analysis, the processes areperformed using data associated to a state value measured in themanufacturing procedure, a parameter such as a control value set in themanufacturing procedure, and a value related to the quality.

In the yield analysis, there is a need to specify a parameter or a setof parameters related to a defective product from among a number ofparameters with respect to a product.

The product is manufactured under various conditions, and various statevalues are taken. Therefore, the number of sets of parameters to beanalyzed is extremely large. Therefore, there is a need of a method ofanalyzing a correlation of the parameters with efficiency. A principalcomponent analysis is known as a method of analyzing the correlation ofthe parameters with efficiency.

SUMMARY OF THE INVENTION

The principal component analysis is performed on an assumption that thevalue of a parameter is a numerical value. Therefore, in a case where anon-numerical value such as a character string is set in the parameter,the correlation of the parameter is not possible to be specified on thebasis of the principal component analysis. In addition, in a case wherea cardinality of the parameter is low, and the parameters do notcorrespond to each other one to one, it is not possible to obtain auseful result in the principal component analysis.

An object of the invention is to perform the yield analysis withefficiency and to improve accuracy.

A representative example of the invention disclosed in the applicationis as follows. In other words, an analysis method performed by acomputer analyzes analysis data acquired from a system which includes aplurality of devices to manufacture a product. The computer includes acalculation device, a storage device connected to the calculationdevice, and a network interface connected to the calculation device. Theanalysis data is configured by a plurality of fields to store aparameter related to the manufacturing of the product and at least onefield to store an evaluation value indicating a quality of the product.The analysis method includes a first step of managing, by the computer,a set of values of each field of the analysis data as a column, a secondstep of analyzing, by the computer, a correspondence from a valuebelonging to a first target column to a value belonging to a secondtarget column to specify a surjection column pair which is a set of thefirst target column and the second target column, the first targetcolumn and the second target column which have a subjectivecorrespondence, a third step of generating, by the computer, graphinformation to manage a first graph of a tree structure indicating aconnection relation of the column by connecting a graph which has nodesof the first target column and the second target column of thesurjection column pair, a fourth step of performing, by the computer, ayield analysis to specify the parameter affecting the evaluation valueusing the graph information.

According to the invention, the yield analysis can be performed withefficiency, and the accuracy can be improved. Objects, configurations,and effects besides the above description will be apparent through theexplanation on the following embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating an exemplary configuration of a systemof a first embodiment;

FIG. 2 is a diagram illustrating an example of a configuration of a datamanagement device of the first embodiment;

FIG. 3 is a diagram illustrating an example of a configuration of ananalysis device of the first embodiment;

FIG. 4 is a diagram for describing a relation between a module and datain the entire system of the first embodiment;

FIG. 5 is a diagram for describing a detail configuration of asurjection graph generating unit of the first embodiment;

FIG. 6 is a diagram for describing a detail configuration of an analysisunit of the first embodiment;

FIG. 7 is a diagram for describing a detail configuration of avisualization unit of the first embodiment;

FIG. 8 is a diagram for describing a notation rule of a data structureof the first embodiment;

FIG. 9 is a diagram illustrating an example of a data structure of acontrol data management information of the first embodiment;

FIG. 10 is a diagram illustrating an example of a data structure ofsensor data management information of the first embodiment;

FIG. 11 is a diagram illustrating an example of a data structure ofprocessing range data management information of the first embodiment;

FIG. 12 is a diagram illustrating an example of a data structure ofstate data management information of the first embodiment;

FIG. 13 is a diagram illustrating an example of a data structure ofquality data management information of the first embodiment;

FIG. 14 is a diagram illustrating an example of a data structure ofanalysis data management information of the first embodiment;

FIG. 15 is a diagram illustrating an example of a data structure ofcardinality information of the first embodiment;

FIG. 16 is a diagram illustrating an example of a data structure ofcolumn correspondence information of the first embodiment;

FIG. 17 is a diagram illustrating an example of a data structure ofsurjection column pair information of the first embodiment;

FIG. 18 is a diagram illustrating an example of a data structure ofsurjection graph information of the first embodiment;

FIG. 19 is a flowchart for describing an example of a process which isperformed by a label generating unit of the analysis device of the firstembodiment;

FIG. 20 is a flowchart for describing an example of a column analysisprocess which is performed by the analysis device of the firstembodiment;

FIG. 21 is a flowchart for describing an example of an appearancefrequency calculating process which is performed by the analysis deviceof the first embodiment;

FIG. 22 is a flowchart for describing an example of a columncorrespondence extracting process which is performed by the analysisdevice of the first embodiment;

FIG. 23 is a flowchart for describing an example of a mappingdetermining process which is performed by the analysis device of thefirst embodiment;

FIG. 24 is a flowchart for describing an example of a bijectiondetermining process which is performed by the analysis device of thefirst embodiment;

FIG. 25 is a flowchart for describing an example of a surjectiondetermining process which is performed by the analysis device of thefirst embodiment;

FIG. 26 is a flowchart for describing an example of a graph generatingprocess which is performed by the analysis device of the firstembodiment;

FIG. 27 is a diagram illustrating an example of the analysis datamanagement information which is input in the column analysis process ofthe first embodiment;

FIG. 28 is a diagram illustrating an example of the surjection columnpair information which is output in the column analysis process of thefirst embodiment;

FIG. 29 is a diagram illustrating an example of correspondence (mapping)between columns which are indicated by the surjection column pairinformation of the first embodiment;

FIG. 30A is a diagram illustrating an example of the surjection graphinformation which is output in the column analysis process of the firstembodiment;

FIG. 30B is a diagram illustrating an example of the surjection graphinformation which is output in the column analysis process of the firstembodiment;

FIG. 31A is a diagram illustrating an example of a graph (tree) which isindicated by the surjection graph information of the first embodiment;

FIG. 31B is a diagram illustrating an example of a graph (tree) which isindicated by the surjection graph information of the first embodiment;

FIG. 32 is a flowchart for describing an example of an analysis dataanalyzing process which is performed by the analysis device of the firstembodiment;

FIG. 33 is a flowchart for describing an example of a scatter diagramgenerating process which is performed by the analysis unit of the firstembodiment;

FIG. 34 is a flowchart for describing an example of a column scorecalculating process which is performed by the analysis unit of the firstembodiment;

FIG. 35 is a diagram illustrating an example of a graph display screen3500 which is displayed by a user interface of the first embodiment; and

FIG. 36 is a flowchart for describing an example of a column erasingprocess which is performed by an analysis device of a second embodiment.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

Hereinafter, embodiments of the invention will be described using thedrawings. However, the invention is not interpreted in a limited way tothe following embodiments. A person skilled in the art can easilyunderstand that the specific configurations may be changed within ascope not departing from the ideas and the spirit of the invention.

The configurations or functions which are identical or similar below inthe invention will be attached with the same symbols, and the redundantdescription will be omitted.

The notations of “first”, “second”, and “third” in this specificationare attached in order to identify the components, but not necessarilyused to indicate the number or the order.

First Embodiment

FIG. 1 is a diagram illustrating an exemplary configuration of a systemof a first embodiment.

The system of the first embodiment is configured by a planning device100, a plurality of controllers 101, a plurality of manufacturingdevices 102, a quality management device 103, a plurality of sensors104, a quality sensor 105, a data management device 106, and an analysisdevice 107. The plurality of controllers 101 include a controller 101-1and a controller 101-2. The plurality of manufacturing devices 102include a manufacturing device 102-1 and a manufacturing device 102-2.The plurality of sensors 104 include a sensor 104-1 and a sensor 104-2.

The planning device 100 is connected to the plurality of controllers 101through a network 120, and is connected to the data management device106 through the network 120 and a network 121. In addition, theplurality of controllers 101, the plurality of sensors 104, and thequality sensor 105 are connected to the data management device 106through the network 121.

The network 120 and the network 121 include a Wide Area Network (WAN)and a Local Area Network (LAN). In addition, a connection method of thenetwork 120 and the network 121 may be a wired or wireless manner.

In the first embodiment, it is assumed a system to which the planningdevice 100 and the data management device 106 are connected. Theplanning device 100 and the data management device 106 are accessible toa factory where a product 111 is manufactured from a material 110.

The planning device 100 generates control data to control themanufacturing device 102, and transmits the control data to thecontroller 101. The planning device 100 is a computer which includes aCPU, a memory, and a network interface. Further, the planning device 100may be realized using a virtual computer.

A planner 10 makes a manufacturing plan of the product 111 and anoperation plan of the manufacturing device 102 using the planning device100.

The controller 101 controls the manufacturing device 102. For example,the controller 101 sets parameters to control the operation of themanufacturing device 102 to the manufacturing device 102. The controller101 is a computer which includes a CPU, a memory, and a networkinterface. In addition, the controller 101 may be realized usingdedicated hardware to control the manufacturing device 102. Further, thecontroller 101 may be realized using the virtual computer.

The sensor 104 measures a value indicating a machining state of thematerial 110 and a value indicating a state of the manufacturing device102. In addition, the sensor 104 transmits a measured value to the datamanagement device 106.

The quality sensor 105 measures an evaluation value which is a value toevaluate the quality of the completed product 111. In addition, thequality sensor 105 transmits the measured evaluation value to the datamanagement device 106. Further, the quality sensor 105 may be connectedto each of the plurality of manufacturing devices 102 in order tomeasure the evaluation value related to the quality of an intermediateproduct.

The data management device 106 manages data related to the manufacturingof the product 111. The configuration of the data management device 106will be described using FIG. 2.

The analysis device 107 performs a yield analysis of the product 111.The configuration of the analysis device 107 will be described usingFIG. 3. An analyst 20 performs the yield analysis of the product 111using the analysis device 107 at any timing.

FIG. 2 is a diagram illustrating an example of the configuration of thedata management device 106 of the first embodiment.

The data management device 106 is a CPU 201, a memory 202, a storagedevice 203, and a network interface 204 as hardware configurations. Therespective hardware configurations are connected through an internalbus.

The CPU 201 performs a program which is stored in the memory 202. TheCPU 201 performs a process according to the program to operate as afunctional unit (module) which realizes a specific function. In thefollowing description, in a case where a process is described with thefunctional unit as a subject, it indicates that the CPU performs theprogram which realizes the functional unit.

The memory 202 stores the program performed by the CPU 201 and datawhich is used by the program. The memory 202 stores the program whichrealizes the functional unit (not illustrated) to manage data.

The storage device 203 is a Hard Disk Drive (HDD) and a Solid StateDrive (SSD), and stores data permanently. The data stored in the storagedevice 203 will be described below. The program stored in the memory 202may be stored in the storage device 203. In a case where the program isperformed, the CPU 201 reads out the program from the storage device203, loads the program to the memory 202, and performs the loadedprogram in the memory 202. In addition, the data stored in the storagedevice 203 may be stored in the memory 202.

The network interface 204 is an interface to communicate with otherdevices through the network.

Herein, the data stored in the storage device 203 will be described. Thestorage device 203 stores control data management information 210,sensor data management information 211, processing range data managementinformation 212, state data management information 213, and quality datamanagement information 214.

The control data management information 210 stores the control data tocontrol the manufacturing device 102. The control data includes, forexample, a parameter such as temperature and pressure which are set tothe manufacturing device 102. The data structure of the control datamanagement information 210 will be described using FIG. 9.

The sensor data management information 211 stores sensor data whichincludes a value indicating the machining state of the material 110measured by the sensor 104. The data structure of the sensor datamanagement information 211 will be described using FIG. 10.

The processing range data management information 212 stores processingrange data. The processing range data is data indicating a time range toacquire time series data related to the product 111 for every type fromthe sensor data which is acquired from the sensor 104 connected to themanufacturing device 102 which manufactures a plural types of products111. The data structure of the processing range data managementinformation 212 will be described using FIG. 11.

The state data management information 213 stores state data whichincludes a value indicating the state of the manufacturing device 102measured by the sensor 104. The data structure of the state datamanagement information 213 will be described using FIG. 12.

The quality data management information 214 stores quality data whichincludes the evaluation value indicating the quality of the product 111measured by the quality sensor 105. The data structure of the qualitydata management information 214 will be described using FIG. 13.

FIG. 3 is a diagram illustrating an example of the configuration of theanalysis device 107 of the first embodiment.

The analysis device 107 includes a CPU 301, a memory 302, a storagedevice 303, a network interface 304, an input device 305, and an outputdevice 306. The respective hardware configurations are connected throughan internal bus.

The CPU 301, the memory 302, the storage device 303, and the networkinterface 304 are the same hardware configuration as those of the CPU201, the memory 202, the storage device 203, and the network interface204.

The input device 305 is a device to input data such as a keyboard, amouse, and a touch panel. The output device 306 is a device to outputdata such as a display and a printer.

Herein, the data stored in the storage device 303 and the program storedin the memory 302 will be described.

The storage device 303 stores analysis data management information 320,surjection column pair information 321, and surjection graph information322.

The analysis data management information 320 stores analysis data towhich data related to the manufacturing of the product 111 is combined.The analysis data is data to be used in the yield analysis, and isconfigured by a field group in which a value related to themanufacturing of the product 111 is stored. The data structure of theanalysis data management information 320 will be described using FIG.14.

The surjection column pair information 321 is mapping information whichindicates correspondence between a set (column) of values of one fieldof the analysis data and a set (column) of values of another field, andis used to manage a combination of sets in which the mapping becomessurjective or bijective. The data structure of the surjection columnpair information 321 will be described using FIG. 17.

In the following description, a combination of columns of which thecorrespondence therebetween becomes bijective is denoted by a bijectioncolumn pair. A combination of columns which have a surjectivecorrespondence is denoted by a surjection column pair. Further, in acase where a combination is denoted by a column pair, it simplyindicates a combination of columns.

The surjection graph information 322 is information to manage a treestructure of graph which is generated by linking the columns on thebasis of the surjection column pair information 321. The data structureof the surjection graph information 322 will be described using FIG. 18.Further, the graph of columns is configured by nodes (columns) and edgeswhich connect the columns which have the surjective correspondence.

The memory 302 stores a program which realizes a label generating unit310, a data combining unit 311, a data correcting unit 312, a surjectiongraph generating unit 313, an analysis unit 314, a visualization unit315, and a user interface 316.

The label generating unit 310 generates label sensor data from thesensor data which is time series data. The label sensor data isformatted to be combined with the state data.

The data combining unit 311 combines the data managed by the datamanagement device 106 to generate the analysis data. In addition, thedata combining unit 311 registers the analysis data in the analysis datamanagement information 320.

The data correcting unit 312 corrects or processes the analysis data invarious types of processes.

The surjection graph generating unit 313 retrieves the bijection columnpair and the surjection column pair to generate the surjection columnpair information 321, and generates the surjection graph information 322on the basis of the surjection column pair information 321.

The analysis unit 314 performs the yield analysis on the basis of thesurjection graph information 322.

The visualization unit 315 generates the display information to presentvarious types of data to the user.

The user interface 316 displays the information to the user, andprovides an interface to receive a user's input.

Further, the respective functional units of the analysis device 107 maybe configured by integrating a plurality of functional units into onefunctional unit, or may configured by dividing one functional unit intoa plurality of functional units according to functions.

FIG. 4 is a diagram for describing a relation between modules and dataof the entire system of the first embodiment.

The data management device 106 acquires the sensor data and the statedata from the sensor 104, acquires the processing range data and thecontrol data from the controller 101, acquires the control data from theplanning device 100, and acquires the quality data from the qualitysensor 105. The data management device 106 stores the acquired data inthe storage device 203.

The analysis device 107 acquires, at an arbitrary timing, the controldata management information 210, the sensor data management information211, the processing range data management information 212, the statedata management information 213, and the quality data managementinformation 214 which are managed by the data management device 106.

The label generating unit 310 of the analysis device 107 generates thelabel sensor data on the basis of the sensor data and the processingrange data, and outputs the label sensor data to the data combining unit311.

The data combining unit 311 combines the control data, the state data,the label sensor data, and the quality data to generate the analysisdata. In addition, the data combining unit 311 stores the generatedanalysis data in the analysis data management information 320.

The surjection graph generating unit 313 acquires the analysis data fromthe analysis data management information 320, extracts thecorrespondence between the columns of the analysis data, and specifies acolumn pair of which the correspondence becomes surjective or bijective.The surjection graph generating unit 313 generates the surjection columnpair information 321 on the basis of the specified column pair. Further,the surjection graph generating unit 313 generates the surjection graphinformation 322 on the basis of the surjection column pair information321. In addition, the surjection graph generating unit 313 outputs acorrect instruction of the analysis data to the data correcting unit 312on the basis of its own instruction or a user's instruction through theuser interface 316.

In a case where the correct instruction of the analysis data isreceived, the data correcting unit 312 corrects the analysis dataaccording to the instruction.

The analysis unit 314 generates the information of a graph (scatterdiagram) on the basis of the analysis data management information 320 asscatter diagram information 400. The graph plots the analysis unit in afeature space which has a field (evaluation value) of the quality dataas an axis. The analysis unit 314 outputs the scatter diagraminformation 400 to the visualization unit 315 at an arbitrary timing. Inaddition, the analysis unit 314 performs the yield analysis on the basisof the analysis data management information 320, the surjection graphinformation 322, and the scatter diagram information 400, and generatesthe analysis result 401.

The visualization unit 315 generates graph display information 402 onthe basis of the surjection graph information 322 and the scatterdiagram information 400.

The user interface 316 displays a screen on the basis of the graphdisplay information 402, and displays a screen on the basis of theanalysis result 401. In addition, the user interface 316 receives thecorrect instruction of the analysis data and the correct instruction ofa parameter which is set to the surjection graph generating unit 313.

FIG. 5 is a diagram for describing a detail configuration of thesurjection graph generating unit 313 of the first embodiment.

The surjection graph generating unit 313 includes an appearancefrequency calculating unit 500, a column correspondence extracting unit501, a bijection determining unit 502, a surjection determining unit503, a graph data generating unit 504, and a noise removing unit 505.

The appearance frequency calculating unit 500 calculates an appearancefrequency of the value of a column. The appearance frequency calculatingunit 500 outputs the calculation result as cardinality information 1500.The data structure of the cardinality information 1500 will be describedusing FIG. 15.

The column correspondence extracting unit 501 extracts a correspondencebetween two columns. The column correspondence extracting unit 501outputs the extraction result of the correspondence as columncorrespondence information 1600. The data structure of the columncorrespondence information 1600 will be described using FIG. 16.

The bijection determining unit 502 retrieves the bijection column pairon the basis of the column correspondence information 1600. Thesurjection determining unit 503 extracts the surjection column pair onthe basis of the column correspondence information 1600. The surjectiongraph generating unit 313 generates the surjection column pairinformation 321 on the basis of the extraction result of the bijectioncolumn pair and the surjection column pair.

The graph data generating unit 504 generates the surjection graphinformation 322 on the basis of the surjection column pair information321.

The noise removing unit 505 removes a value which acts as a noise fromthe values of the columns in a case where the column pair is extracted.

FIG. 6 is a diagram for describing a detail configuration of theanalysis unit 314 of the first embodiment.

The analysis unit 314 includes a correlation analysis unit 600, aclassification accuracy evaluating unit 601, a distance calculating unit602, and an analysis order determining unit 603.

The correlation analysis unit 600 generates the scatter diagraminformation 400 to manage the location of the analysis data in thefeature space which has the evaluation value as an axis.

The classification accuracy evaluating unit 601 calculates a valueindicating the analysis accuracy on the basis of the surjection columnpair information 321 and the scatter diagram information 400 in a casewhere the analysis data in the feature space is classified by payingattention to an arbitrary column.

The distance calculating unit 602 calculates a distance between columnsin the tree structure of graph.

The analysis order determining unit 603 determines a processing numberof a combination of the column to analyze the correlation in the yieldanalysis on the basis of the surjection column pair information 321, avalue indicating a classification accuracy, and a distance of the columnin structure data.

FIG. 7 is a diagram for describing a detail configuration of thevisualization unit 315 of the first embodiment.

The visualization unit 315 includes a display data generating unit 700.The display data generating unit 700 generates the graph displayinformation 402 on the basis of the scatter diagram and the surjectiongraph information 322.

Next, the data structure of data which is handled in the system will bedescribed. First, the notation of the data structure used in thisspecification will be described.

FIG. 8 is a diagram for describing a notation rule of the data structureof the first embodiment.

In the first embodiment, the description will be given about a JSON datastructure as an example. Then, the following notation rule is applied.

Rule 1 indicates the notation rule of an array. Since an array havingelements of a set as values is used in the first embodiment, a notation800 is denoted as a notation 801 using a label of a set. The uppercaseletter indicates a symbol of a set, and the lowercase letter indicatesan element of the set. Further, a symbol indicating a subset will bedenoted using the same letter as the set in this specification.

Rule 2 indicates a notation rule of an object. In the notation rule ofthe object, the notation 810 is also denoted as the notation 811 usingthe label of the set.

Rule 3 and Rule 4 indicate the notation rules in which Rule 1 and Rule 2are combined.

Next, the data structure of data and information will be described usingFIGS. 9 to 18.

FIG. 9 is a diagram illustrating an example of the data structure of thecontrol data management information 210 of the first embodiment.

The control data management information 210 stores the control datawhich is configured by an FID 901, a PID 902, a temperature 903, and apressure 904.

The FID 901 is a field to store identification information of themanufacturing device 102. The PID 902 is a field to store theidentification information of the product 111. The temperature 903 andthe pressure 904 are fields to store values of the parameters to controlthe manufacturing device 102.

FIG. 10 is a diagram illustrating an example of the data structure ofthe sensor data management information 211 of the first embodiment.

The sensor data management information 211 stores the sensor data whichis configured by an FID 1001, a time 1002, Sensor 1 (1003), and Sensor 2(1004). The FID 1001 is the same field as the FID 901.

The time 1002 is a field to store a time (time stamp) at which the valueincluded in the sensor data is measured. Sensor 1 (1003) is a field tostore the value which is measured by the sensor 104-1. Sensor 2 (1004)is a field to store the value which is measured by the sensor 104-2.

Further, in a case where the sensors 104 measure different type ofvalues, the sensor data includes fields to store the respective values.Further, in a case where the sensors 104 measures values at differentintervals, the sensor data management information 211 may be stored ineach sensor 104.

FIG. 11 is a diagram illustrating an example of the data structure ofthe processing range data management information 212 of the firstembodiment.

The processing range data management information 212 stores theprocessing range data which is configured by an FID 1101, a PID 1102, aT_START 1103, and a T_END 1104. The FID 1101 and the PID 1102 are thesame field as the FID 901 and the PID 902.

The T_START 1103 is a field which stores a time indicating a startingpoint of a range cut out the value related to the product 111corresponding to the PID 1102 from the sensor data which is acquiredfrom the manufacturing device 102 corresponding to the FID 1101. TheT_END 1104 is a field which stores a time indicating an ending point ofa range cut out the value related to the product 111 corresponding tothe PID 1102 from the sensor data which is acquired from themanufacturing device 102 corresponding to the FID 1101.

FIG. 12 is a diagram illustrating an example of the data structure ofstate data management information 213 of the first embodiment.

The state data management information 213 stores the state data which isconfigured by an FID 1201, a PID 1202, a temperature 1203, and apressure 1204. The FID 1201 and the PID 1202 are the same field as theFID 901 and the PID 902.

The temperature 1203 and the pressure 1204 are fields to storemeasurement values of the parameters to control the manufacturing device102.

FIG. 13 is a diagram illustrating an example of the data structure ofquality data management information 214 of the first embodiment.

The quality data management information 214 stores the quality datawhich is configured by a PID 1301, a defect rate 1302, and a materialstrength 1303. In the quality data management information 214illustrated in FIG. 13, the quality data of a unit of product is stored.The PID 1301 is the same field as the PID 902.

The defect rate 1302 and the material strength 1303 are fields to storethe evaluation values of the product 111. Specifically, the defect rate1302 is a field to store a ratio of the products 111 which aredetermined as defective products. The material strength 1303 is a fieldto store a strength of the product 111.

FIG. 14 is a diagram illustrating an example of the data structure ofanalysis data management information 320 of the first embodiment.

The analysis data management information 320 stores the analysis datawhich is configured by a PID 1401, control data 1402, state data 1403,and quality data 1404. The analysis data management information 320illustrated in FIG. 14 stores the analysis data in a unit of the product111. The PID 1401 is the same field as the PID 902.

The control data 1402 is a field to store the control data related tothe product 111 corresponding to the PID 1401. In the control data 1402,the control data acquired from the planning device 100 and the pluralityof controllers 101 is stored.

The state data 1403 is a field to store the state data related to theproduct 111 corresponding to the PID 1401. In the state data 1403, thestate data acquired from the plurality of sensors 104 is stored.

The quality data 1404 is a field to store the quality data related tothe product 111 corresponding to the PID 1401.

A row 1410 indicates one piece of analysis data. A column 1411 is a setof values of a specific field, that is, a column. In the column, anattribute name of the value stored in the field is set as a column name.One column forms one or more categories.

FIG. 15 is a diagram illustrating an example of the data structure ofcardinality information 1500 of the first embodiment.

The cardinality information 1500 is information to manage the categoryof each column and the appearance frequency of a value belonging to thecategory.

For example, the data structure of the cardinality information 1500 maybe defined as denoted in a schema 1501. The cardinality information 1500defined in the schema 1501 includes data associated with the appearancefrequency of the category of the column and the value belonging to thecategory with respect to each column name. A sample 1502 indicates aspecific example of the cardinality information 1500.

FIG. 16 is a diagram illustrating an example of the data structure ofcolumn correspondence information 1600 of the first embodiment.

The column correspondence information 1600 is information to manage thecorrespondence from the category of the column to the category of theother column.

For example, the data structure of the column correspondence information1600 may be defined as denoted in a schema 1601. The columncorrespondence information 1600 defined in the schema 1601 includes datawhich has the value of Column A as a key and has the set of Column Bcorresponding to the key as a reference value. A sample 1602 indicates aspecific example of the column correspondence information 1600.

FIG. 17 is a diagram illustrating an example of the data structure ofsurjection column pair information 321 of the first embodiment.

The surjection column pair information 321 is information to manage thebijection column pair and the surjection column pair. For example, thedata structure of the surjection column pair information 321 may bedefined as denoted in a schema 1701.

The surjection column pair information 321 defined in the schema 1701includes a data group of a unit of column. The data group of a unit ofcolumn includes data indicating the appearance frequency of the value ofeach category of the column, data indicating the column and the othercolumns of the bijection column pair, and data indicating the column andthe other columns of the surjection column pair. A sample 1702 indicatesa specific example of the surjection column pair information 321.

FIG. 18 is a diagram illustrating an example of a data structure ofsurjection graph information 322 of the first embodiment.

The surjection graph information 322 is information to manage a treestructure of graph which indicates a relation of the surjection orbijection of the columns. For example, the data structure of thesurjection graph information 322 may be defined as denoted in a schema1801.

The surjection graph information 322 defined in the schema 1801indicates a graph of the column which has the root node as the highestnode. The node of the graph of column is assigned with the name of thecolumn. A sample 1802 illustrates a specific example of the surjectiongraph information 322.

Next, the process of performing the analysis device 107 will bedescribed.

First, an updating process of the analysis data management information320 performed by the analysis device 107 will be described. The analysisdevice 107 performs the updating process of the analysis data managementinformation 320 when received an instruction from the analyst 20 or insynchronization therewith.

The analysis device 107 acquires the sensor data management information211, the processing range data management information 212, the statedata management information 213, the control data management information210, and the quality data management information 214 from the datamanagement device 106. The label generating unit 310 of the analysisdevice 107 performs the process illustrated in FIG. 19 in a case wherevarious types of information are acquired. With this process, it ispossible to convert the sensor data into the label sensor data of aformat of combining with other data.

The data combining unit 311 of the analysis device 107 combines thecontrol data, the state data, and the label sensor data which arematched in the combination of the FID and the PID, and combines thequality data which is matched with the PID of the data combined with thePID 1301 so as to generate the analysis data. The data combining unit311 stores the generated analysis data in the analysis data managementinformation 320.

FIG. 19 is a flowchart for describing an example of a process which isperformed by a label generating unit 310 of the analysis device 107 ofthe first embodiment.

The label generating unit 310 acquires the sensor data managementinformation 211 and the processing range data management information 212(Step S101).

Next, the label generating unit 310 selects one piece of processingrange data from the processing range data management information 212(Step S102). For example, the label generating unit 310 selects theprocessing range data in an order from the top of the processing rangedata management information 212.

Next, the label generating unit 310 retrieves the target sensor data tobe processed with reference to the sensor data management information211 on the basis of the selected processing range data (Step S103).

Specifically, the label generating unit 310 retrieves the sensor data inwhich the FID 1001 is matched with the FID 1101 of the selectedprocessing range data, and the time 1002 falls within a time rangedesignated by the T_START 1103 and the T_END 1104.

Next, the label generating unit 310 puts the target sensor data togetherto generate the label sensor data (Step S104).

For example, the following process is performed. The label generatingunit 310 calculates an average value of each of Sensor 1 (1003) andSensor 2 (1004) of the target sensor data. The label generating unit 310generates the label sensor data which is configured by the FID, the PID,and the fields of Sensor 1 and Sensor 2. The label generating unit 310sets the FID 1101 to the FID, and sets the PID 1102 to the PID. Inaddition, the label generating unit 310 sets the average value to eachfield of Sensor 1 and Sensor 2.

Further, a method of putting the sensor data together is not limited tothe above configuration. For example, a maximum value or a minimum valuemay be set to the field.

Next, the label generating unit 310 determines whether all theprocessing range data registered in the processing range data managementinformation 212 is processed (Step S105).

In a case where the processes of all the processing range dataregistered in the processing range data management information 212 arenot completed, the label generating unit 310 returns to Step S102, andperforms the same process.

In a case where the processes of all the processing range dataregistered in the processing range data management information 212 arecompleted, the label generating unit 310 outputs the generated labelsensor data to the data combining unit 311 (Step S106). Thereafter, thelabel generating unit 310 ends the process. Further, in the firstembodiment, the label sensor data is output as the state data.

Next, the description will be given about a column analysis processwhich is performed by the analysis device 107 to generate the surjectioncolumn pair information 321 and the surjection graph information 322.

FIG. 20 is a flowchart for describing an example of a column analysisprocess which is performed by the analysis device 107 of the firstembodiment.

The analysis device 107 reads out the analysis data managementinformation 320 which is stored in the storage device 303 (Step S201).

Next, the analysis device 107 performs an appearance frequencycalculating process to generate the cardinality information 1500 (StepS202). The details of the appearance frequency calculating process willbe described using FIG. 21.

Next, the analysis device 107 performs a column correspondenceextracting process to generate the column correspondence information1600 (Step S203). The details of the column correspondence extractingprocess will be described using FIG. 22.

Next, the analysis device 107 performs a mapping determining process togenerate the surjection column pair information 321 (Step S204). Thedetails of the mapping determining process will be described using FIG.23.

Next, the analysis device 107 performs a graph generating process togenerate the surjection graph information 322 (Step S205). The detailsof the graph generating process will be described using FIG. 26.

FIG. 21 is a flowchart for describing an example of an appearancefrequency calculating process which is performed by the analysis device107 of the first embodiment.

The surjection graph generating unit 313 selects a target column fromthe columns of the analysis data management information 320 (Step S301).

Next, the surjection graph generating unit 313 generates the category ofthe target column (Step S302).

Specifically, the surjection graph generating unit 313 analyzes the typeof the value of the target column to generate the category of the targetcolumn. For example, in a case where the values of the columns are “a1,a1, a2, a3, a3, a4, a4, a4”, the category of the target column becomes“a1”, “a2”, “a3”, and “a4”.

Further, in a case where the value stored in the target column is ananalogue value, the type of the value is significantly increased.Therefore, in a case where the value stored in the target column is ananalogue value, the surjection graph generating unit 313 sets a range ofthe values and generates a category on the basis of the range of thevalues. For example, a range of equal to or more than 10 and less than20 is set for the category “a1”.

Next, the surjection graph generating unit 313 calculates a cardinalityof the target column (Step S303).

Specifically, the surjection graph generating unit 313 calculates thenumber of categories as the cardinality. Further, the cardinality is avalue of 1 or more.

Next, the surjection graph generating unit 313 determines whether thecardinality is 1 or more (Step S304).

In a case where it is determined that the cardinality is 1, thesurjection graph generating unit 313 erases the target column from theanalysis data management information 320 through the data correctingunit 312. Thereafter, the surjection graph generating unit 313 proceedsto Step S310. Since the column having a cardinality of “1” is not neededto check the relation with the other columns, the column is erased fromthe analysis data management information 320.

In a case where it is determined that the cardinality is not “1”, thesurjection graph generating unit 313 selects a target category fromamong the categories of the target column (Step S306).

Next, the surjection graph generating unit 313 calculates the number ofvalues belonging to the target category as the appearance frequency(Step S307).

In a case where the values of the target column are “a1, a1, a2, a3, a3,a4, a4, a4”, and the target category is “a4”, the appearance frequencybecomes “3”.

Next, the surjection graph generating unit 313 determines whether allthe categories of the target column are processed (Step S308).

In a case where it is determined that all the categories of the targetcolumn are not completely processed, the surjection graph generatingunit 313 returns to Step S306.

In a case where it is determined that all the categories of the targetcolumn are completely processed, the surjection graph generating unit313 updates the cardinality information 1500 (Step S309).

Specifically, the data configured by the name of the target column, thename of the category, and the appearance frequency is added to thecardinality information 1500 by the surjection graph generating unit313.

After the process of Step S305 or Step S309 is completed, the surjectiongraph generating unit 313 determines whether the processes of all thecolumns of the analysis data management information 320 are completed(Step S310).

In a case where it is determined that the processes of all the columnsof the analysis data management information 320 are not completed, thesurjection graph generating unit 313 returns to Step S301.

In a case where it is determined that the processes of all the columnsof the analysis data management information 320 are completed, thesurjection graph generating unit 313 outputs the cardinality information1500 to the memory 302 (Step S311), and ends the appearance frequencycalculating process.

FIG. 22 is a flowchart for describing an example of the columncorrespondence extracting process which is performed by the analysisdevice 107 of the first embodiment.

The surjection graph generating unit 313 generates a column pair (P, C)which is a combination of the columns of the analysis data managementinformation 320 (Step S401). Column P indicates a parent column, andColumn C indicates a child column. In the first embodiment, thesurjection graph generating unit 313 analyzes the correspondence fromthe category of the parent column to the category of the child column.Therefore, a column pair (temperature, pressure) and a column pair(pressure, temperature) are considered as different column pairs.

Further, the information of the column pair is also used in the otherprocesses. Therefore, the surjection graph generating unit 313 performscontrols not to erase the information from the memory 302 even after thecolumn correspondence extracting process ends.

Next, the surjection graph generating unit 313 selects a target columnpair from among the column pairs (Step S402).

Next, the surjection graph generating unit 313 selects a target categoryfrom among the categories of the parent column with reference to thecardinality information 1500 (Step S403).

Next, the surjection graph generating unit 313 specifies the category ofthe child column which is set to the target category of the parentcolumn (Step S404).

Specifically, the surjection graph generating unit 313 extracts a set ofa value belonging to the target category and a value of the child columnin a unit of row. The surjection graph generating unit 313 specifies acategory to which the value of the child column belongs with respect tothe cardinality information 1500. With the above processes, the categoryof the child column set to the target category of the parent column isspecified.

Next, the surjection graph generating unit 313 updates the columncorrespondence information 1600 (Step S405).

Specifically, the data configured by the target category and thecategory of the specified child column is added to the columncorrespondence information 1600 by the surjection graph generating unit313.

Next, the surjection graph generating unit 313 determines whether theprocesses of all the categories of the parent column are completed (StepS406).

In a case where it is determined that the processes of all thecategories of the parent column are not completed, the surjection graphgenerating unit 313 returns to Step S403.

In a case where it is determined that the processes of all thecategories of the parent column are completed, the surjection graphgenerating unit 313 determines whether the processes of all the columnpairs are completed (Step S407).

In a case where it is determined that the processes of all the columnpairs are not completed, the surjection graph generating unit 313returns to Step S402.

In a case where it is determined that the processes of all the columnpairs are completed, the surjection graph generating unit 313 outputsthe column correspondence information 1600 to the memory 302 (StepS408), and ends the column correspondence extracting process.

FIG. 23 is a flowchart for describing an example of the mappingdetermining process which is performed by the analysis device 107 of thefirst embodiment.

The surjection graph generating unit 313 selects a target column pair(Step S501). Herein, a process result of Step S401 is used as it is.

The surjection graph generating unit 313 performs a bijectiondetermining process on the target column pair (Step S502), and performsa surjection determining process (Step S503). The details of thebijection determining process will be described using FIG. 24, and thedetails of the surjection determining process will be described usingFIG. 25.

Next, the surjection graph generating unit 313 determines whether theprocesses of all the column pairs are completed (Step S504).

In a case where it is determined that the processes of all the columnpairs are not completed, the surjection graph generating unit 313returns to Step S501.

In a case where it is determined that the processes of all the columnpairs are completed, the surjection graph generating unit 313 outputsthe surjection column pair information 321 to the storage device 303(Step S505), and ends the surjection determining process.

Specifically, the surjection graph generating unit 313 registers thedata of the bijection column pair and the surjection column pair asJSON-format data in the surjection column pair information 321 withreference to the list described below.

FIG. 24 is flowchart for describing an example of the bijectiondetermining process which is performed by the analysis device 107 of thefirst embodiment.

The surjection graph generating unit 313 determines whether thecardinality of the parent column and the cardinality of the child columnof the target column pair are equal with reference to the cardinalityinformation 1500 (Step S601).

In a case where it is determined that the cardinality of the parentcolumn and the cardinality of the child column are not equal, thesurjection graph generating unit 313 ends the bijection determiningprocess.

In a case where it is determined that the cardinality of the parentcolumn and the cardinality of the child column are equal, the surjectiongraph generating unit 313 determines whether each category of the parentcolumn has one category of the child column corresponding to onecategory of the parent column with reference to the columncorrespondence information 1600 (Step S602). In the followingdescription, the condition that there is one category of the childcolumn corresponding to one category of the parent column is referred toas a first condition.

The first condition is a condition to determine whether the category ofthe child column is uniquely set with respect to the category of theparent column. As another expression, the first condition is a conditionto determine whether the correspondence from the category of the parentcolumn to the category of the child column becomes mapping.

In a case where it is determined that at least one category of theparent column does not satisfy the first condition, the surjection graphgenerating unit 313 ends the bijection determining process.

In a case where it is determined that all the categories of the parentcolumn satisfy the first condition, the surjection graph generating unit313 determines whether each category of the child column has onecategory of the parent column corresponding to one category of the childcolumn with reference to the column correspondence information 1600(Step S603). In the following description, the condition that onecategory of the parent column corresponds to one category of the childcolumn is referred to as a second condition.

The second condition is a condition to determine whether the category ofthe parent column is uniquely set with respect to the category of thechild column.

In a case where it is determined that at least one category of the childcolumn does not satisfy the second condition, the surjection graphgenerating unit 313 ends the bijection determining process.

In a case where it is determined that all the categories of the childcolumn satisfy the second condition, the surjection graph generatingunit 313 adds a record to store the data of the column pair which hasthe bijective correspondence to the list (Step S604), and ends thebijection determining process.

Specifically, the surjection graph generating unit 313 acquires dataindicating the correspondence between the categories of the column pairfrom the column correspondence information 1600. Herein, the dataindicating the correspondence from the category of the parent column tothe category of the child column and the data indicating thecorrespondence from the category of the child column to the category ofthe parent column are acquired. The surjection graph generating unit 313adds a record which is configured by the names of the parent column andthe child column, the data indicating the correspondence between thecategories of the acquired column pair, and the character string“injection” to the list.

In addition, the surjection graph generating unit 313 performs controlto remove the column pair in which the parent column and the childcolumn of the target column pair are replaced from the processingtarget. In a case where the correspondence between the columns isbijective, there is no need to perform the process on the column pair inwhich the parent column and the child column of the target column pairare replaced.

FIG. 25 is flowchart for describing an example of the surjectiondetermining process which is performed by the analysis device 107 of thefirst embodiment.

The surjection graph generating unit 313 determines whether thecorrespondence of the target column pair is bijective (Step S701).

Specifically, the surjection graph generating unit 313 determineswhether the target column pair is registered as the bijection columnpair with reference to the list.

In a case where it is determined that the correspondence of the targetcolumn pair is bijective, the surjection graph generating unit 313 endsthe surjection determining process.

In a case where it is determined that the correspondence of the targetcolumn pair is not bijective, the surjection graph generating unit 313determines whether each category of the parent column satisfies thefirst condition (Step S702).

In a case where it is determined that at least one category of theparent column does not satisfy the first condition, the surjection graphgenerating unit 313 ends the surjection determining process.

In a case where it is determined that all the categories of the parentcolumn satisfy the first condition, the surjection graph generating unit313 adds a record to store the data of the column pair which has thesurjective correspondence to the list (Step S703), and ends thesurjection determining process.

Specifically, the surjection graph generating unit 313 acquires dataindicating the correspondence between the categories of the column pairfrom the column correspondence information 1600. Herein, the dataindicating the correspondence from the category of the parent column tothe category of the child column is acquired. The surjection graphgenerating unit 313 adds a record which is configured by the names ofthe parent column and the child column, the data indicating thecorrespondence between the categories of the acquired column pair, andthe character string “surjection” to the list.

FIG. 26 is a flowchart for describing an example of a graph generatingprocess which is performed by the analysis device 107 of the firstembodiment.

The surjection graph generating unit 313 initializes the surjectiongraph information 322 (Step S801).

Next, the surjection graph generating unit 313 selects a target recordfrom the list (Step S802).

Next, the surjection graph generating unit 313 generates graph data ofthe target record (Step S803).

Specifically, the surjection graph generating unit 313 generates data ofthe graph in which the nodes corresponding to the parent column and thechild column are connected by edges.

Further, in a case where the correspondence between columns isbijective, the thickness and color of the edge connecting the columnsmay be set to be different from other edges. In addition, the thicknessand color of the edge may be set on the basis of an overlapping degreeof the category of the parent column with respect to the category of thechild column. With this configuration, a combination of parameters to befocused in the analysis data may be presented to the analyst 20.

Next, the surjection graph generating unit 313 selects the target columnfrom among the terminal columns corresponding to a leaf node of thegraph with reference to the surjection graph information 322 (StepS804).

Next, the surjection graph generating unit 313 determines whether thetarget column is matched to the parent column of the target record (StepS805).

In a case where it is determined that the target column is matched tothe parent column of the target record, the surjection graph generatingunit 313 updates the surjection graph information 322 (Step S806).Thereafter, the surjection graph generating unit 313 proceeds to StepS808.

Specifically, the surjection graph generating unit 313 adds the graphdata of the target record to the surjection graph information 322 toconnect the parent column of the target record as the child column ofthe target column. In other words, the child column of the target recordis connected to the target column.

Further, in a case where the target record includes the character string“injection”, the following process is performed.

In a case where the target column is matched to the parent column of thetarget record, the surjection graph generating unit 313 determineswhether the column directly connected to the target column is differentfrom the child column of the target record.

In a case where it is determined that the column directly connected tothe target column is different from the child column of the targetrecord, the surjection graph generating unit 313 adds the graph data ofthe target record to the surjection graph information 322 to connect theparent column of the target record as the child column of the targetcolumn. In other words, the child column of the target record isconnected to the target column.

The above-described process is a process of preventing that the parentcolumn of a certain column is connected as the child column of thecolumn.

Further, the data may be set such that the display method of the columnvaries according to the number of branches from one column to the othercolumns. For example, it is considered that the column of which thenumber of branches is 3 or more is highlighted. With this configuration,an important parameter in the analysis data may be presented to theanalyst 20.

In a case where it is determined that the target column is not matchedto the parent column of the target record, the surjection graphgenerating unit 313 updates the surjection graph information 322 (StepS807). Thereafter, the surjection graph generating unit 313 proceeds toStep S808.

Specifically, the surjection graph generating unit 313 adds the graphdata of the target record to the surjection graph information 322 toconnect the parent column of the target record to the root node.

In Step S808, the surjection graph generating unit 313 determineswhether the processes are completed on all the terminal columns (StepS808).

In a case where it is determined that the processes are not completed onall the terminal columns, the surjection graph generating unit 313returns to Step S804.

In a case where it is determined that the processes are completed on allthe terminal columns, the surjection graph generating unit 313determines whether the processes are completed on all the records of thelist (Step S809).

In a case where it is determined the processes are not completed on allthe records of the list, the surjection graph generating unit 313returns to Step S802.

In a case where it is determined that the processes are completed on allthe records of the list, the surjection graph generating unit 313outputs the surjection graph information 322 to the storage device 303(Step S810). The data of the graph generated by the above processes isdenoted as forward tree structure data.

Further, in a case where the surjection graph information 322 is empty,the surjection graph generating unit 313 adds the graph data of thetarget record to the surjection graph information 322.

Further, the process of Step S805 may be replaced to the processdescribed below. In other words, the surjection graph generating unit313 determines whether the parent column of the target column is matchedto the child column of the target record. In this case, in Step S806,the surjection graph generating unit 313 adds the graph data of thetarget record to the surjection graph information 322 to connect thechild column of the target record as the parent column of the targetcolumn. The data of the graph generated by the above-described processesis denoted as backward tree structure data.

Herein, the column analysis process will be described using a specificexample. FIG. 27 is a diagram illustrating an example of the analysisdata management information 320 which is input in the column analysisprocess of the first embodiment. FIG. 28 is a diagram illustrating anexample of the surjection column pair information 321 which is output inthe column analysis process of the first embodiment. FIG. 29 is adiagram illustrating an example of correspondence (mapping) betweencolumns which are indicated by the surjection column pair information321 of the first embodiment. FIGS. 30A and 30B are diagrams illustratingan example of the surjection graph information 322 which is output inthe column analysis process of the first embodiment. FIGS. 31A and 31Bare diagrams illustrating an example of a graph (tree) which isindicated by the surjection graph information 322 of the firstembodiment.

The analysis data management information 320 illustrated in FIG. 27stores six pieces of analysis data of which the names are “A”, “B”, “C”,“D”, and “E” of the column.

The result of the appearance frequency calculating process with respectto the analysis data management information 320 illustrated in FIG. 27.For the column “A”, the category “a1” and the appearance frequency “1”,the category “a2” and the appearance frequency “1”, the category “a3”and the appearance frequency “2”, and the category “a4” and theappearance frequency “2” are obtained. For the column “B”, the category“b1” and the appearance frequency “2”, the category “b2” and theappearance frequency “2”, and the category “b3” and the appearancefrequency “2” are obtained. For the column “C”, the category “c1” andthe appearance frequency “2”, and the category “c2” and the appearancefrequency “4” are obtained. For the column “D”, the category “d1” andthe appearance frequency “1”, and the category “d2” and the appearancefrequency “5” are obtained. For the column “E”, the category “e1” andthe appearance frequency “3”, and the category “e2” and the appearancefrequency “3” are obtained.

As a result of performing the column correspondence extracting processand the mapping determining process on the analysis data managementinformation 320 illustrated in FIG. 27, the surjection column pairinformation 321 illustrated in FIG. 28 is generated. The surjectioncolumn pair information 321 illustrated in FIG. 28 is information todefine the correspondence (mapping) between the columns illustrated inFIG. 29.

In addition, as a result of the graph generating process on the analysisdata management information 320 illustrated in FIG. 27, the surjectiongraph information 322 indicating the forward tree structure dataillustrated in FIG. 30A is generated. Further, the surjection graphinformation 322 illustrated in FIG. 30A is information to define thegraph illustrated in FIG. 31A. Further, in a case where the process ofgenerating the backward tree structure data is performed, the surjectiongraph information 322 illustrated in FIG. 30B is generated. Thesurjection graph information 322 illustrated in FIG. 30B is informationto define the graph illustrated in FIG. 31B.

Next, the yield analysis performed by the analysis device 107 will bedescribed.

FIG. 32 is a flowchart for describing an example of the yield analysiswhich is performed by the analysis device 107 of the first embodiment.

In a case where the surjection column pair information 321 is output, orin a case where an instruction is received from the analyst 20, theanalysis unit 314 performs the yield analysis periodically.

First, the analysis unit 314 performs a scatter diagram generatingprocess (Step S901). The details of the scatter diagram generatingprocess will be described using FIG. 33.

Next, the analysis unit 314 selects the target column with reference tothe analysis data management information 320 (Step S902).

Next, the analysis unit 314 performs a column score calculating processusing the target column and the scatter diagram (Step S903). The columnscore calculating process is a process for calculating a column score toevaluate an analysis result of the analysis data in the feature space onthe basis of the target column. The details of the column scorecalculating process will be described using FIG. 34.

Next, the analysis unit 314 determines whether all the columns of theanalysis data management information 320 are completely processed (StepS904).

In a case where it is determined that the processes of all the columnsof the analysis data management information 320 are not completed, theanalysis unit 314 returns to Step S902.

In a case where it is determined that all the columns of the analysisdata management information 320 are completely processed, the analysisunit 314 selects the target column pair (Step S905).

Next, the analysis unit 314 calculates a distance in the graph of thecolumn between the columns of the target column pair (Step S906).

Specifically, the analysis unit 314 calculates the number of columnspassing through when moving from one column to another column of thetarget column pair as a distance. For example, in the case of the graphof the column illustrated in FIG. 31A, the distance of a column pair (A,C) is calculated to be “2”, the distance of a column pair (C, D) iscalculated to be “3”, and the distance of a column pair (B, E) iscalculated to be “3”.

Next, the analysis unit 314 determines whether the processes of all thecolumn pairs are completed (Step S907).

In a case where it is determined that the processes of all the columnpairs are not completed, the analysis unit 314 returns to Step S905.

In a case where it is determined that the processes of all the columnpairs are completed, the analysis unit 314 starts the loop process ofthe column pair again. In other words, the analysis unit 314 selects thetarget column pair (Step S908). Further, the analysis unit 314normalizes the distance of each column pair to start the loop process ofthe column pair. For example, the analysis unit 314 normalizes thedistance of the column pair to make a maximum value of the distance be“1”.

Next, the analysis unit 314 calculates a total score on the basis of thecolumn score of each column of the target column pair and the distance(Step S909).

The analysis unit 314 calculates the total score on the basis of thefollowing Formula (1) for example. Herein, St indicates the total score,S[Ci] indicates the column score of a column Ci, and D[Ci, Cj] indicatesthe distance between the columns Ci and Cj on the graph of the column.In addition, Dmax indicates a maximum value of the distance, and kindicates an arbitrary coefficient.

$\begin{matrix}\left\lbrack {{Formula}\mspace{14mu} 1} \right\rbrack & \; \\{\mspace{205mu}{{St} = {\left( {{S\left\lbrack C_{i} \right\rbrack} + {S\left\lbrack C_{j} \right\rbrack}} \right) + {k\;\frac{D\left\lbrack {C_{i},C_{j}} \right\rbrack}{D\;\max}}}}} & (1)\end{matrix}$

Next, the analysis unit 314 determines whether the processes of all thecolumn pairs are completed (Step S910).

In a case where it is determined that the processes of all the columnpairs are not completed, the analysis unit 314 returns to Step S908.

In a case where it is determined that the processes of all the columnpairs are completed, the analysis unit 314 determines an analysis orderof the column pair on the basis of the total score, and performs theyield analysis (Step S911).

Specifically, the analysis unit 314 determines the analysis order of thecolumn pair to analyze the column pair having a high total score withpriority. A well-known technique may be employed for the yield analysis,and thus the detailed description thereof will be omitted.

In this way, if the analysis device 107 in the yield analysis isexpanded in the number of analyzing column pairs, the column pairs ofthe analysis target may be narrowed down. In addition, the analysisdevice 107 performs the analysis on the basis of the analysis order, sothat a useful result can be obtained at a high speed. In other words,according to the first embodiment, it is possible to perform the yieldanalysis with efficiency, and to improve the accuracy.

FIG. 33 is a flowchart for describing an example of a scatter diagramgenerating process which is performed by the analysis unit 314 of thefirst embodiment.

First, the analysis unit 314 sets the feature space (Step S1001).

Specifically, the analysis unit 314 sets the feature space which has afield to store the value indicating the quality as an axis. Further, allthe fields may be not used. In this case, the field of the feature spaceis designated in advance.

Next, the analysis unit 314 selects target analysis data from theanalysis data management information 320 (Step S1002).

Next, the analysis unit 314 determines coordinates of the targetanalysis data in the feature space (Step S1003).

Specifically, the analysis unit 314 determines a set of the values ofthe fields corresponding to the axis of the feature space to thecoordinates of the target analysis data of the feature space.

Next, the analysis unit 314 determines a display color of the targetanalysis data (Step S1004). Specifically, the following process isperformed.

The analysis unit 314 selects the target column from among the columnscorresponding to the control data and the state data which form theanalysis data.

The analysis unit 314 calculates an index form the value of the fieldcorresponding to the target column. For example, the analysis unit 314calculates the category as the index. The analysis unit 314 determines acolor which is assigned to the index. The color is set randomly forexample. However, the color is determined not to be overlapped with thecolor to which a different index is assigned. The analysis unit 314performs the same process on each field.

Further, the display color may be determined for a specific column. Inthis case, a column to determine the display color is designated inadvance. Hitherto, the process of Step S1004 has been described.

Next, the analysis unit 314 updates the scatter diagram information 400(Step S1005).

Specifically, the analysis unit 314 adds data configured by thecoordinates, the display color, and the index to the scatter diagraminformation 400. Further, in a case where one piece of scatter diagraminformation 400 is generated for one column, the analysis unit 314 addsdata configured by the coordinates and the display color to the scatterdiagram information 400 of one column.

Next, the analysis unit 314 determines whether the processes of all theanalysis data is completed (Step S1006).

In a case where it is determined that the processes of all the analysisdata are not completed, the analysis unit 314 returns to Step S1002.

In a case where it is determined that the processes of all the analysisdata are completed, the analysis unit 314 outputs the scatter diagraminformation 400 to the storage device 303 (Step S1007), and ends thescatter diagram generating process.

FIG. 34 is a flowchart for describing an example of a column scorecalculating process which is performed by the analysis unit 314 of thefirst embodiment.

The analysis unit 314 acquires the scatter diagram information 400corresponding to the target column, and performs a clustering on thebasis of the target column (Step S1101). Further, the invention is notlimited to the method of clustering. For example, k-means may be used.

Next, the analysis unit 314 calculates a clustering score to evaluatethe result of the clustering (Step S1102).

For example, in a case where the clustering is performed several times,the analysis unit 314 calculates a separation degree of the clusterswhenever performing the clustering.

Next, the analysis unit 314 determines whether an ending condition issatisfied (Step S1103).

For example, the analysis unit 314 determines whether the number oftimes of the clustering is equal to or more than a threshold.

In a case where it is determined that the ending condition is notsatisfied, the analysis unit 314 returns to Step S1101.

In a case where it is determined that the ending condition is satisfied,the analysis unit 314 calculates the column score (Step S1104).

For example, the analysis unit 314 calculates the average value of theclustering score as the column score. Further, in a case where theclustering is not performed several times, the analysis unit 314 maycalculate the clustering score as the column score.

FIG. 35 is a diagram illustrating an example of a graph display screen3500 which is displayed by a user interface 316 of the first embodiment.

The graph display screen 3500 is a screen to display a scatter diagramwhich is color-corded in the graph of the column on the basis of theindex of the column.

The visualization unit 315 generates graph display information 402 todisplay the graph display screen 3500 on the basis of the surjectiongraph information 322 and the scatter diagram information 400.

The visualization unit 315 may display only the graph of the columnaccording to a request of the analyst 20. In this case, thevisualization unit 315 generates the graph display information 402 todisplay the graph of the column illustrated in FIG. 31A or 31B on thebasis of the surjection graph information 322 FIG. 31A or 31B.

The analyst 20 can grasp the data structure such as a relation betweenthe fields of the analysis data by referring to the graph of the columnillustrated in FIGS. 31A and 31B. For example, it can be visuallyunderstood that the value of the field corresponding to Column B andColumn D is uniquely determined with respect to the value of the fieldcorresponding to Column A. In addition, new information such as a casewhere the control data is different even the products are equal can befound out visually and understood.

In addition, the analyst 20 can visually grasp the parameter causing avariation of the quality by referring to the graph illustrated in FIG.35. In addition, it is possible to be understood whether the data iswrong using the parameters close to each other on the graph.

In the process described above, the bijection column pair and thesurjection column pair are processed separately, but the process may beperformed only using the surjection column pair. In this case, thebijection determining process is not performed. In addition, in thegraph generating process, the surjection graph generating unit 313performs the process of Step S806 only in a case where the target columnis matched to the parent column of the target record, and the columndirectly connected to the target column is different from the childcolumn of the target record in order to prevent the parent column of acertain column from being connected as the child column.

As described above, according to the first embodiment, it is possible toperform the yield analysis with efficiency, and to improve the accuracy.In addition, the analyst 20 can visually grasp the feature of theanalysis data and the important parameters in the yield analysis.

Second Embodiment

In a second embodiment, a method of generating the surjection columnpair information 321 is partially different. Hereinafter, the secondembodiment will be described focusing on the difference from the firstembodiment.

In a case where the number of categories of the column is large, thecorrespondence between the columns is less likely to be surjective orbijective. Therefore, the graph of the column in which the columns areconnected in a multistage manner is not generated. Therefore, it is notpossible to present information useful to the analyst 20.

Therefore, in the second embodiment, a process in which the data isprocessed such that the correspondence between the columns becomessurjective or bijective is performed.

The system configuration and the device configuration of the secondembodiment are the same as those of the first embodiment. The datastructure of information of the second embodiment is the same as that ofthe first embodiment.

The process performed by the label generating unit 310 of the secondembodiment is the same as that of the first embodiment. In the secondembodiment, the column analysis process is different partially.Specifically, the analysis device 107 performs a category erasingprocess after the process of Step S202. FIG. 36 is a flowchart fordescribing an example of a column erasing process which is performed byan analysis device 107 of a second embodiment.

The surjection graph generating unit 313 selects the target column withreference to the cardinality information 1500 (Step S1201).

Next, the surjection graph generating unit 313 calculates a threshold ofthe target column (Step S1202).

Specifically, the surjection graph generating unit 313 calculates anappearance probability of the value belonging to each category of thetarget column with reference to the cardinality information 1500. Thesurjection graph generating unit 313 calculates the threshold of thetarget column using a center value of the appearance probability. Forexample, a value obtained by multiplying 0.1 to the center value of theappearance probability is calculated as the threshold. The describedabove threshold is used to remove the category to which a low appearanceprobability belongs as a noise.

Further, the above calculation method is an example, and the inventionis not limited thereto. For example, the threshold may be calculated onthe basis of the appearance frequency. In addition, the threshold may bedetermined by performing a machine learning.

Next, the surjection graph generating unit 313 selects the targetcategory from among the category of the target column (Step S1203).

Next, the surjection graph generating unit 313 determines whether theappearance probability of the value belonging to the target category isless than the threshold (Step S1204).

In a case where it is determined that the appearance probability of thevalue belonging to the target category is equal to or more than thethreshold, the surjection graph generating unit 313 proceeds to StepS1206.

In a case where it is determined that the appearance probability of thevalue belonging to the target category is less than the threshold, thesurjection graph generating unit 313 erases the target category from thecardinality information 1500.

In Step S1206, the surjection graph generating unit 313 determineswhether all the categories of the target column are processed (StepS1206).

In a case where it is determined that the processes of all thecategories of the target column are not completed, the surjection graphgenerating unit 313 returns to Step S1203.

In a case where it is determined that the processes of all thecategories of the target column are completed, the surjection graphgenerating unit 313 determines whether the processes of all the columnsare completed (Step S1207).

In a case where it is determined that the processes of all the columnsare not completed, the surjection graph generating unit 313 returns toStep S1201.

In a case where it is determined that the processes of all the columnsare completed, the surjection graph generating unit 313 ends thecategory erasing process.

It is possible to increase the number of column pairs retrieved as thesurjection column pair or the bijection column pair by erasing thecategory which becomes a noise.

In the second embodiment, the column correspondence extracting processis different partially. Specifically, after the process of Step S402,the surjection graph generating unit 313 reduces the number ofcategories of the column pair. In a case where the number of categoriesof the parent column and the child column is significantly large, thecorrespondence between the columns is less likely to be surjective orbijective. Then, the surjection graph generating unit 313 performs theclustering of the category of each column using an Infinite RelationalModel (IRM), and reduces the number of categories. The IRM is awell-known technique, and thus the description thereof will be omitted.

According to the second embodiment, the column pairs of the bijectioncolumn pair and the surjection column pair can be increased. Therefore,it is possible to generate the graph of the column in which the columnsare connected in a multistage manner. With this configuration, it ispossible to present information useful to the analyst 20.

Further, the invention is not limited to the above embodiments, butincludes various modifications. In addition, for example, theembodiments have been described about the configuration in detail inorder to help with understanding on the invention, but the invention isnot limited to the one equipped with all the configurations. Inaddition, some of the configurations of each embodiment may be added,deleted, or replaced with respect to the other configurations.

In addition, some or all of the configurations, the functions, theprocessing units, and processing devices may be realized in hardware bydesigning with an integrated circuit for example. In addition, theinvention may be realized by a software program code which realizes thefunctions of the embodiments. In this case, a recording medium recordedwith the program code is provided to a computer, and a processor of thecomputer reads out the program code stored in the recording medium. Inthis case, the program code itself read out of the recording medium isused to realize the functions of the above embodiments. The program codeitself and the recording medium storing the program code is configuredin the invention. As a recording medium to supply such a program code,for example, there are a flexible disk, a CD-ROM, a DVD-ROM, a harddisk, a Solid State Drive (SSD), an optical disk, a magneto-opticaldisk, a CD-R, a magnetic tape, a nonvolatile memory card, and a ROM.

In addition, the program code to realize the functions of eachembodiment may be embedded by a wide program such as assembler, C/C++,perl, Shell, PHP, Java (registered trademark) or a script language.

Further, the software program code to realize the functions of theembodiment is distributed through a network, and stored in a recordingunit such as a hard disk and a memory of the computer or a recordingmedium such as a CD-RW and a CD-R. The processor provided in thecomputer may read and perform the program code stored in the recordingunit or the recording medium.

In the above embodiments, only control lines and information linesconsidered to be necessary for explanation are illustrated, but not allthe control lines and the information lines for a product areillustrated. All the configurations may be connected to each other.

What is claimed is:
 1. An analysis method performed by a computer whichanalyzes analysis data acquired from a system, the system including aplurality of devices to manufacture a product, wherein the computerincludes a calculation device, a storage device connected to thecalculation device, and a network interface connected to the calculationdevice, wherein the analysis data is configured by a plurality of fieldsto store a parameter related to the manufacturing of the product and atleast one field to store an evaluation value indicating a quality of theproduct, the analysis method, comprising: a first step of managing, bythe computer, a set of values of each field of the analysis data as acolumn; a second step of analyzing, by the computer, a correspondencefrom a value belonging to a first target column to a value belonging toa second target column to specify a surjection column pair which is aset of the first target column and the second target column, the firsttarget column and the second target column which have a subjectivecorrespondence; a third step of generating, by the computer, graphinformation to manage a first graph of a tree structure indicating aconnection relation of the first target column and the second targetcolumn by connecting a graph which has nodes of the first target columnand the second target column of the surjection column pair; and a fourthstep of performing, by the computer, a yield analysis to specify theparameter affecting the evaluation value using the graph information. 2.The analysis method according to claim 1, wherein the second stepincludes a fifth step of generating, by the computer, one or morecategories by classifying values belonging to the column, and a sixthstep of analyzing, by the computer, a correspondence from a valuebelonging to the category of the first target column to a valuebelonging to the category of the second target column, and wherein thethird step includes selecting, by the computer, a first targetsurjection column pair, generating, by the computer, a graph which hasnodes of the first target column and the second target column of thefirst target surjection column pair, selecting, by the computer, aterminal column corresponding to a leaf node of the first graph,determining, by the computer, whether the first target column of thefirst target surjection column pair is matched to the terminal column,and connecting, by the computer, the second target column of the firsttarget surjection column pair to a leaf node corresponding to theterminal column in a case where the first target column of the firsttarget surjection column pair is matched to the terminal column.
 3. Theanalysis method according to claim 2, wherein the second step includesspecifying, by the computer, a bijection column pair which is a set ofthe first target column and the second target column of which thecorrespondence becomes bijective, and specifying, by the computer, thesurjection column pair from among a set of columns except the bijectioncolumn pair, and wherein the third step includes selecting, by thecomputer, a target bijection column pair, generating, by the computer, agraph which has nodes of the first target column and the second targetcolumn of the target bijection column pair, selecting, by the computer,the terminal column, determining, by the computer, whether the firsttarget column of the target bijection column pair is matched to theterminal column, and the second target column of the target bijectioncolumn pair is different from a column corresponding to a node to whicha node corresponding to the terminal column is connected, andconnecting, by the computer, a node corresponding to the second targetcolumn of the target bijection column pair to a leaf node correspondingto the terminal column in a case where the first target column of thetarget bijection column pair is matched to the terminal column, and thesecond target column of the target bijection column pair is differentfrom a column corresponding to a node to which a node corresponding tothe terminal column is connected.
 4. The analysis method according toclaim 2, wherein the fifth step includes setting, by the computer, arange of a value in a case where a value belonging to the column is ananalogue value, and generating, by the computer, the category on thebasis of the range of the value.
 5. The analysis method according toclaim 2, wherein the sixth step includes analyzing, by the computer, acorrespondence from the value belonging to the category of the firsttarget column to the value belonging to the category of the secondtarget column after the numbers of categories of the first target columnand categories of the second target column are reduced.
 6. The analysismethod according to claim 2, wherein the fourth step includesgenerating, by the computer, data of a second graph indicating aposition of the analysis data in a feature space which has theevaluation value as an axis, performing, by the computer, clustering ofthe analysis data in the feature space which is paid attention to aspecific column with respect to the column, calculating, by thecomputer, a first score to evaluate the clustering on the basis of aresult of the clustering, selecting, by the computer, a second targetsurjection column pair, calculating, by the computer, a distance betweenthe first target column and the second target column of the secondtarget surjection column pair in the second graph, calculating, by thecomputer, a second score to determine an analysis order of thesurjection column pair in the yield analysis on the basis of thedistance and the first score of each of the first target column and thesecond target column of the second target surjection column pair, anddetermining, by the computer, an analysis order of the surjection columnpair in the yield analysis on the basis of the second score.
 7. Acomputer which analyzes analysis data acquired from a system, the systemincluding a plurality of devices to manufacture a product, wherein theanalysis data is configured by a plurality of fields to store aparameter related to manufacturing the product, and at least one fieldto store an evaluation value indicating a quality of the product,wherein the computer includes a calculation device, a storage deviceconnected to the calculation device, a network interface connected tothe calculation device, a graph generating unit which analyzes acorrespondence between columns, the column being a set of values of eachfield of the analysis data, and generates a graph connecting a columnassociated to the correspondence on the basis of a result of theanalysis, and an analysis unit which performs a yield analysis tospecify the parameter affecting the evaluation value on the basis of thegraph and the analysis data, wherein the graph generating unit isconfigured to analyze a correspondence from a value belonging to a firsttarget column to a value belonging to a second target column to specifya surjection column pair which is a set of the first target column andthe second target column, the first target column and the second targetcolumn which have a subjective correspondence, and generate graphinformation to manage a first graph of a tree structure indicating aconnection relation of the column by connecting a graph which has nodesof the first target column and the second target column of thesurjection column pair, and wherein the analysis unit performs the yieldanalysis using the graph information.
 8. The computer according to claim7, wherein the graph generating unit is configured to generate one ormore categories by classifying values belonging to the column to analyzea correspondence from a value belonging to the category of the firsttarget column to a value belonging to the category of the second targetcolumn in a case where a correspondence from the value belonging to thefirst target column to the value belonging to the second target columnis analyzed, select a first target surjection column pair in a casewhere the graph information is generated, generate a graph which hasnodes of the first target column and the second target column of thefirst target surjection column pair, select a terminal columncorresponding to a leaf node of the first graph, determine whether thefirst target column of the first target surjection column pair ismatched to the terminal column, and connect a node corresponding to thesecond target column of the first target surjection column pair to aleaf node corresponding to the terminal column in a case where the firsttarget column of the first target surjection column pair is matched tothe terminal column.
 9. The computer according to claim 8, wherein thegraph generating unit is configured to specify a bijection column pairwhich is a set of the first target column and the second target columnwhich have a bijective correspondence, and specify the surjection columnpair from among a set of columns except the bijection column pair in acase where a correspondence from the value belonging to the first targetcolumn to the value belonging to the second target column is analyzed,select a target bijection column pair in a case where the graphinformation is generated, generate a graph which has nodes of the firsttarget column and the second target column of the target bijectioncolumn pair, select the terminal column, determine whether the firsttarget column of the target bijection column pair is matched to theterminal column, and the second target column of the target bijectioncolumn pair is different from a column corresponding to a node to whicha node corresponding to the terminal column is connected, and connect anode corresponding to the second target column of the target bijectioncolumn pair to a node corresponding to the terminal column in a casewhere the first target column of the target bijection column pair ismatched to the terminal column, and the second target column of thetarget bijection column pair is different from a column corresponding toa node to which a node corresponding to the terminal column isconnected.
 10. The computer according to claim 8, wherein the graphgenerating unit is configured to set a range of a value in a case wherea value belonging to the column which is a generation target of thecategory is an analogue value, and generate the category on the basis ofthe range of the value.
 11. The computer according to claim 8, whereinthe graph generating unit is configured to reduce the numbers of thecategories of the first target column and the categories of the secondtarget column, and analyze a correspondence from the value belonging tothe category of the first target column to the value belonging to thecategory of the second target column.
 12. The computer according toclaim 8, wherein the analysis unit is configured to generate data of asecond graph indicating a position of the analysis data in a featurespace which has the evaluation value as an axis, perform a clustering ofthe analysis data in the feature space which is paid attention to aspecific column with respect to the column, calculate a first score toevaluate the clustering on the basis of a result of the clustering,select a second target surjection column pair, calculate a distancebetween the first target column and the second target column of thesecond target surjection column pair in the second graph, calculate asecond score to determine an analysis order of the surjection columnpair in the yield analysis on the basis of the distance and the firstscore of each of the first target column and the second target column ofthe second target surjection column pair, and determine an analysisorder of the surjection column pair in the yield analysis on the basisof the second score.